An important pillar of Linked Open Government Data is to be able to mix datasets by using common ontologies in order to infer new knowledge. The open government datasets to be mashed-up by developers may be subject to distinct licenses, legal notices, terms of use, and applicable law and regulations from multiple jurisdictions. Within this complex ecosystem there is a need to create semi-automatic tools supported by an ontology to help technical reusers of Public Sector Information to utilize datasets according to their intended purpose and in compliance with the legal obligations that govern the rights to reuse the data. Unfortunately, some researchers may avoid considering all the legal frameworks that apply in the domain of Open Government Data and limit their investigation to only the area of licenses. To enable wider, compliant utilisation of mashed-up open data, we have analysed the European Union (EU) legal framework of reuse of Public Sector Information (PSI), the EU Database Directive and copyright framework and other legal sources (e.g., licenses, legal notices, terms of use) that can apply to open government Datasets. From this deep analysis we now model several major concepts in an Ontology of Open Government Data Licenses Framework for a Mash-up Model (OGDL4M). There have been earlier ontologies for creative commons or open licenses, but they did not anticipate the other legal constraints that arise from Open Government regulations. The OGDL4M ontology will be used for qualifying datasets in order to improve the accuracy of their legal annotation. The Ontology also aims to connect each applicable legal rule to official legal texts in order to direct legal experts and reusers to primary sources. This paper aims to present the modules of the OGDL4M ontology in depth and to describe some preliminary evaluation.

Mockus, M., Palmirani, M. (2017). Legal Ontology for Open Government Data Mashups. Los Alamitos, CA : IEEE.

Legal Ontology for Open Government Data Mashups

MOCKUS, MARTYNAS;PALMIRANI, MONICA
2017

Abstract

An important pillar of Linked Open Government Data is to be able to mix datasets by using common ontologies in order to infer new knowledge. The open government datasets to be mashed-up by developers may be subject to distinct licenses, legal notices, terms of use, and applicable law and regulations from multiple jurisdictions. Within this complex ecosystem there is a need to create semi-automatic tools supported by an ontology to help technical reusers of Public Sector Information to utilize datasets according to their intended purpose and in compliance with the legal obligations that govern the rights to reuse the data. Unfortunately, some researchers may avoid considering all the legal frameworks that apply in the domain of Open Government Data and limit their investigation to only the area of licenses. To enable wider, compliant utilisation of mashed-up open data, we have analysed the European Union (EU) legal framework of reuse of Public Sector Information (PSI), the EU Database Directive and copyright framework and other legal sources (e.g., licenses, legal notices, terms of use) that can apply to open government Datasets. From this deep analysis we now model several major concepts in an Ontology of Open Government Data Licenses Framework for a Mash-up Model (OGDL4M). There have been earlier ontologies for creative commons or open licenses, but they did not anticipate the other legal constraints that arise from Open Government regulations. The OGDL4M ontology will be used for qualifying datasets in order to improve the accuracy of their legal annotation. The Ontology also aims to connect each applicable legal rule to official legal texts in order to direct legal experts and reusers to primary sources. This paper aims to present the modules of the OGDL4M ontology in depth and to describe some preliminary evaluation.
2017
2017 International Conference for E-Democracy and Open Government
1
12
Mockus, M., Palmirani, M. (2017). Legal Ontology for Open Government Data Mashups. Los Alamitos, CA : IEEE.
Mockus, Martynas; Palmirani, Monica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/592720
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